Clustering signifiers in a semantics graph

ABSTRACT

Clustering signifiers in a semantics graph can comprise coarsening a semantics graph associated with an enterprise communication network containing a plurality of nodes into a number of sub-graphs containing supernodes; partitioning each of the number of sub-graphs into a number of clusters; and iteratively refining the number of clusters to reduce an edge-cut of the semantics graph, based on the number of clusters.

BACKGROUND

Enterprises can move workloads from a centrally hosted and managedcenter to network systems by offering users (e.g., employees orcustomers) services over the network. A service, as used herein, caninclude an intangible commodity offer to users of a network. Suchservices can include computing resources (e.g., storage, memory,processing resources) and/or computer-readable instructions (e.g.,programs).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart illustrating an example of a method forclustering signifiers in a semantics graph according to the presentdisclosure.

FIG. 2A illustrates an example of a coarsening process for clusteringsignifiers in a semantics graph according to the present disclosure.

FIG. 2B illustrates an example of a partitioning process for clusteringsignifiers in a semantics graph according to the present disclosure.

FIG. 2C illustrates an example of a refining process for clusteringsignifiers in a semantics graph according to the present disclosure.

FIG. 3 is a diagram illustrating an example of a system according to thepresent disclosure.

DETAILED DESCRIPTION

An enterprise may use an enterprise network, such as a cloud systemand/or Internet network, to distribute workloads. An enterprise network,as used herein, can include a network system to offer services to usersof the enterprise (e.g., employees and/or customers). A user may benefitfrom another user's experience with a particular service. However, dueto the distributed nature of an enterprise network, users may havedifficulty in sharing knowledge, such as services experiences.

In some situations, an enterprise may use an enterprise communicationnetwork to assist users of an enterprise network in sharing knowledge,learning from other users' services experiences, and searching forcontent relevant to the enterprise and/or the enterprise network. Theenterprise communication network, as used herein, can include anelectronic communication network to connect users of the network torelevant content. Users of the enterprise communication network cancontribute to the enterprise communication network through a range ofactivities such as posting service-related entries, linking entries tocontent available on internal and external domains, reading comments,commenting on comments, and/or voting on users' entries. Thereby, theenterprise communication network can act as a social network associatedwith the enterprise, services offered by the enterprise, and/ordocuments associated with the enterprise, among other topics.

However, the range of activities that users can contribute to anenterprise communication network can result in the enterprisecommunication network containing unstructured content. Due to theunstructured nature of the content, a general purpose search engine maynot properly function to allow users to search for content in theenterprise communication network. General purpose search engines mayutilize measures such as back-links and/or clicks to define a qualityand reputation of searched content. In an enterprise communicationnetwork, the quality and reputations of content may not be proportionalto the number of back-links and/or clicks.

In contrast, in examples of the present disclosure, a relatedness ofcontent within the enterprise communication network can be identified byclustering signifiers in a semantics graph. A semantics graph can beassociated with the enterprise communication network in that thesemantics graph can include signifiers gathered from content within theenterprise communication network. The signifiers can be identified bygathering content using a search tool and extracting signifiers from thegathered content. A relatedness of the identified signifiers can bedefined by clustering signifiers in the semantics graph and can assistusers in searching for content within the enterprise communicationnetwork.

Examples of the present disclosure may include methods, systems, andcomputer-readable and executable instructions and/or logic. An examplemethod for clustering signifiers in a semantics graph can includecoarsening a semantics graph associated with an enterprise communicationnetwork containing a plurality of nodes into a number of sub-graphscontaining supernodes, partitioning each of the number of sub-graphsinto a number of clusters, and iteratively refining the number ofclusters to reduce the edge-cut of the weighted semantics graph, basedon the number of clusters.

In the following detailed description of the present disclosure,reference is made to the accompanying drawings that form a part hereof,and in which is shown by way of illustration how examples of thedisclosure may be practiced. These examples are described in sufficientdetail to enable those of ordinary skill in the art to practice theexamples of this disclosure, and it is to be understood that otherexamples may be utilized and the process, electrical, and/or structuralchanges may be made without departing from the scope of the presentdisclosure.

The figures herein follow a numbering convention in which the firstdigit or digits correspond to the drawing figure number and theremaining digits identify an element or component in the drawing.Similar elements or components between different figures may beidentified by the use of similar digits. Elements shown in the variousexamples herein can be added, exchanged, and/or eliminated so as toprovide a number of additional examples of the present disclosure.

In addition, the proportion and the relative scale of the elementsprovided in the figures are intended to illustrate the examples of thepresent disclosure, and should not be taken in a limiting sense. As usedherein, “a number of” an element and/or feature can refer to one or moreof such elements and/or features.

FIG. 1 is a flow chart illustrating an example of a method 100 forclustering signifiers in a semantics graph according to the presentdisclosure. The method 100 can be used to cluster signifiers in asemantics graph associated with an enterprise communication networkcontaining a plurality of signifiers. An enterprise communicationnetwork, as used herein, can include a network connecting a plurality ofusers and content through a range of activities. The activities can berelated to a services network of the enterprise (e.g., enterprisenetwork). For example, the activities can include postingservice-related entries, linking entries to internal enterprise domainsand/or external domains, and/or reading, commenting, and/or voting onother user's entries. In various examples of the present disclosure, theenterprise communication network can be a sub-portion of and/orcontained within the enterprise network.

A semantics graph can allow users of the enterprise communicationnetwork to search for content within the enterprise communicationnetwork. A general purpose search engine may not be able to search forcontent in the enterprise communication network given the unstructurednature of the content. Such a search engine may function by defining aquality and reputation of content (e.g., domains) based on a number ofback-links (e.g., links from other content) and/or clicks by a user.However, content in the enterprise communication network may not haveproportional back-links and/or clicks to the quality and/or reputationof the content. In some instances, content in the enterprisecommunication network may not have measureable back-links and/or clicks(e.g., email). In order to search content within the enterprisecommunication network, semantics of signifiers within the enterprisenetwork can be clustered.

A semantics graph, as used here, can include a data structurerepresenting concepts that are related to one another. The concepts caninclude words, phrases, and/or acronyms for instance. The semanticsgraph can include a plurality of nodes connected by a plurality ofedges. A node can include a vertex representing a signifier. An edge canbe incident to a vertex (e.g., an edge can connect a first vertex to asecond vertex). A signifier, as used herein, can include a word, phrase,and/or acronym within the content of the enterprise network and/or theenterprise communication network. The signifiers can be gathered, invarious examples, using search tools (e.g., web crawlers) and extractiontools (e.g., extractors). A signifier associated with the enterprisecommunication network can include a signifier gathered from theenterprise network and/or the enterprise communication network.

The semantics graph can be represented by the following:G=(S,E).wherein S can denote a number of nodes (e.g. signifiers), and E candenote a number of edges (e can denote each individual edge) such thateach of the number of edges e connects the nodes in S. In other words,the G=(S,E) graph can be said to have a vertex set S and an edge set E.

As used herein, a semantics graph can include a weighted semanticsgraph. A weighted semantics graph can include edges weighted to denote anumerical value associated with the edge. For instance, an edgeconnecting a pair of related signifiers (e.g., a first signifier and asecond signifier) can be weighted with a positive numerical valuerepresenting a distance metric between the pair of related signifiers.As discussed further herein, a vertex weight can be calculated for theweighted graph. A vertex weight can include a numerical value associatedwith a node. A weighted semantics graph can be represented by thefollowing:G=(S,E,w).

At 102, the method 100 for clustering signifiers in a semantics graphcan include coarsening the semantics graph for the enterprisecommunication network containing a plurality of nodes into a number ofsub-graphs containing supernodes. A supernode, as used herein, caninclude a number of nodes and the incident edges associated with each ofthe number of nodes. An edge incident to a first node can include anedge that connects the first node to a second node.

A semantics graph can include a number of subsets of nodes. A subset ofnodes can include nodes that are related to one another by semanticproximity. Semantic proximity, as used herein, can include semanticrelatedness and/or semantic similarity wherein a metric is assigned to apair of signifiers indicating their relatedness. For instance, semanticproximity and/or semantic relatedness can indicate how frequently a pairof signifiers is used together. Coarsening the semantics graph caninclude condensing the subset of nodes into a number of supernodes suchthat the number of incident edges belonging to different subsets ofnodes (e.g. the edge-cut) is minimized. For example, a subset of nodescan include four nodes representing the words “a”, “the”, “wireless” and“connection”, and the edges connecting the four nodes. A supernode canbe created (e.g., supernode X) representing the words “a”, “the”, and“wireless”, connected to the node “connection”. In this example, theedge-cut can be reduced from 3 to 1 given that supernode X is onlyconnected to one other node.

In a number of examples, a sub-graph can be iteratively coarsened tofurther reduce the edge-cut. In other words, coarsening the semanticsgraph can include condensing the semantics graph into a number ofsupernodes a number of times, until no two edges in each sub-graph areincident to the same node and/or supernode.

For example, a semantics graph (e.g., G₀ wherein ‘0’ can denote that thegraph has not been coarsened) can be iteratively coarsened into a numberof sub-graphs (e.g., G₁, G₂, . . . , G_(m)) such that the number ofnodes and/or supernodes in each sub-graph G₁, G₂, . . . , G_(m)decreases during each coarsening iteration (e.g., S₀>S₁>S₂> . . .>S_(m)).

At 104, the method 100 for clustering signifiers in a semantics graphcan include partitioning each of the number of sub-graphs into a numberof clusters. In a number of examples, each of the number of sub-graphscan be partitioned into a number of clusters using a vertex weight forthe sub-graph. The vertex weight for the sub-graph can include the sumof the weights of the vertices in the sub-graph. In a number ofexamples, if a vertex is not assigned a weight, the vertex can beassigned a weight of 1. The sub-graph can then be partitioned into twoclusters, wherein each cluster has a vertex weight equal to one half ofthe vertex weight for the sub-graph.

In a number of examples, each of the number of sub-graphs can beiteratively partitioned into a number of clusters using a vertex weight.For instance, a sub-graph can include a vertex weight of 500 and can bepartitioned into two clusters each with a vertex weight of 250, andfurther partitioned into four clusters each with a vertex weight of 125.In other words, each of the number of sub-graphs G₁, G₂, . . . , G_(m)can be partitioned into a number of clusters, wherein each cluster is anequal portion the sub-graph. In a number of examples, the number ofsub-graphs can be partitioned into a number of distinct clusters byusing an algorithm (e.g. the Kernighan-Lin algorithm).

At 106, the method 100 for clustering signifiers in a semantics graphcan include iteratively refining the number of clusters to reduce anedge-cut of the semantics graph, based on the number of clusters.Refining the semantics graph can include further reducing the edge-cutof the semantics graph using local refinement heuristics. For instance,after each of the number of sub-graphs are partitioned into a number ofclusters, the edge-cut of the semantics graph can be determined. Tofurther reduce the edge-cut of the number of clusters in the semanticsgraph, a first node and/or supernode from a first cluster can beswitched with a second node and/or supernode from a second cluster. Ifthe edge-cut of the number of clusters after the switch is more than theedge-cut of the number of clusters before the switch, the switch can bereversed. Conversely, if the edge-cut of the number of clusters afterthe switch is less than the edge-cut of the number of clusters beforethe switch, the switch can be maintained. This process can beiteratively repeated until the edge-cut of the number of clusters cannotbe further reduced.

FIG. 2A illustrates an example of a coarsening process 210 forclustering signifiers in a semantics graph according to the presentdisclosure. A coarsening process 210 for clustering signifiers caninclude coarsening a subset of nodes in a semantics graph. As shown at211, a subset of nodes can include the nodes “a” 201-1, “the” 201-2,“connection” 201-3, and “wireless” 201-4 connected by edges 203-1,203-2, and 203-3. Edges 203-1, 203-2, and 203-3 can be weighted with anumerical value, for instance, representing a distance metric and/or aco-occurrence metric. In a number of examples, nodes 201-1, 201-2,and/or 201-3 can be weighted with a numerical value.

As shown at 213, a first sub-graph containing supernode 205-1 can becreated by condensing nodes 201-1 and 201-2 during a first coarseningiteration. A supernode can include a number of nodes and the edgesconnecting the number of nodes. For instance, supernode 205-1 caninclude nodes 201-2 and 201-4, as well as edge 203-2.

As shown at 215, a second sub-graph containing supernode 209-1 can becreated by condensing supernode 205-1 with node 201-3 during a secondcoarsening iteration, such that the number of incident edges is reducedto 1. For instance, supernode 209-1 can include nodes 201-2, 209-1 and201-4, as well as edges 203-2 and 203-3, and can be connected to node201-1 by edge 203-1.

Each coarsening iteration of the semantics graph can include a matchingof nodes within the semantics graph. A matching of nodes within thesemantics graph can include forming maximal matchings, wherein amatching is maximal if any edge in the semantics graph that is not inthe matching has at least one of its nodes matched. In other words, eachcoarsening iteration of the semantics graph can include a series ofmatching iterations to reduce the number of nodes.

For instance, a weighted semantics graph can start with 10,000 nodes.After a first coarsening iteration, the weighted semantics graph caninclude a number of sub-graphs containing a number of supernodes and/ornodes, wherein the total number of supernodes and/or nodes in theweighted semantics graph can be 9,000. After the second coarseningiteration, the weighted semantics graph can include a number ofsub-graphs containing a number of supernodes and/or nodes, wherein thetotal number of supernodes and/or nodes can be 8,200.

FIG. 2B illustrates an example of a partitioning process 212 forclustering signifiers in a semantics graph according to the presentdisclosure. The partitioning process 212 can include computing abisection of the number of sub-graphs. In other words, the partitioningprocess 212 can include partitioning each of the number of sub-graphsinto a number of clusters, wherein each of the number of clustersincludes half of the total vertex weight of the sub-graph it waspartitioned from.

For instance, as shown in FIG. 2B, sub-graph 217 can include a node“receive” 221-1 that has a vertex weight of 1 (indicated in FIG. 2B bythe parenthetical number following the word “receive”), connected tosupernode 221-2 which includes the nodes “router” 221-3, “fiber” 221-4,“statistic” 221-5, “remote” 221-6, and “network” 221-7, with vertexweights of 13, 12, 4, 2, and 28, respectively. The partitioning process212 can include creating a partitioned graph 219 by partitioningsub-graph 217 into two clusters 223 and 225, wherein cluster 223 andcluster 225 each have a vertex weight of 30 (e.g., 28+2, 1+12+4+13) orhalf of the total vertex weight of 60 of sub-graph 217 (e.g.,1+13+12+28+4+2). That is, cluster 223 can include nodes 221-6 and 221-7,whereas cluster 225 can include nodes 221-1, and supernode 221-8 whereinsupernode 221-8 includes nodes 221-5, 221-4, and 221-3. In a number ofexamples, the partitioning process 212 can include computing ahigh-quality bisection of the number of sub-graphs using theKernighan-Lin algorithm.

FIG. 2C illustrates an example of a refining process 214 for clusteringsignifiers in a semantics graph according to the present disclosure. Therefining process 214 can include switching a node or subset of nodesbetween a pair of clusters to reduce the edge-cut of the partitionedgraph 219 (illustrated in FIG. 2B).

For instance, the edge-cut of the partitioned graph 219 can include thenumerical value of 5 indicating the number of incident edges belongingto different subsets of nodes (e.g. edge-cut). In contrast, the edge-cutof the refined graph 231 can include the numerical value of 4,indicating a reduced edge-cut. In a number of examples, the refinedgraph 219 can be iteratively refined by switching a pair of nodes and/orsubsets of nodes between clusters 227 and 229 until the edge-cut of therefined graph 231 cannot be reduced any further.

FIG. 3 illustrates a block diagram of an example of a system 322according to the present disclosure. The system 322 can utilizesoftware, hardware, firmware, and/or logic to perform a number offunctions.

The system 322 can be any combination of hardware and programinstructions configured to cluster signifiers in a semantics graph. Thehardware, for example, can include a processing resource 324, and/or amemory resource 328 (e.g., computer-readable medium (CRM), machinereadable medium (MRM), database, etc.) A processing resource 324, asused herein, can include any number of processors capable of executinginstructions stored by a memory resource 328. Processing resource 324may be integrated in a single device or distributed across devices. Theprogram instructions (e.g., computer-readable instructions (CRI)) caninclude instructions stored on the memory resource 328 and executable bythe processing resource 324 to implement a desired function (e.g., tocoarsen a semantics graph associated with an enterprise communicationnetwork, etc.).

The memory resource 328 can be in communication with a processingresource 324. A memory resource 328, as used herein, can include anynumber of memory components capable of storing instructions that can beexecuted by processing resource 324. Such memory resource 328 can benon-transitory CRM. Memory resource 328 may be integrated in a singledevice or distributed across devices. Further, memory resource 328 maybe fully or partially integrated in the same device as processingresource 324 or it may be separate but accessible to that device andprocessing resource 324. Thus, it is noted that the system 322 may beimplemented on a user and/or a client device, on a server device and/ora collection of server devices, and/or on a combination of the userdevice and the server device and/or devices.

The processing resource 324 can be in communication with a memoryresource 328 storing a set of CRI executable by the processing resource324, as described herein. The CRI can also be stored in remote memorymanaged by a server and represent an installation package that can bedownloaded, installed, and executed. The system 322 can include memoryresource 328, and the processing resource 324 can be coupled to thememory resource 328.

Processing resource 324 can execute CRI that can be stored on aninternal or external memory resource 328. The processing resource 324can execute CRI to perform various functions, including the functionsdescribed with respect to FIGS. 1, 2A, 2B, and 2C. For example, theprocessing resource 324 can execute CRI to cluster signifiers in asemantics graph.

The CRI can include a number of modules 330, 332, 334. The number ofmodules 330, 332, 334 can include CRI that when executed by theprocessing resource 324 can perform a number of functions.

The number of modules 330, 332, 334 can be sub-modules of other modules.For example, the coarsening module 330 and the partitioning module 332can be sub-modules and/or contained within the same computing device. Inanother example, the number of modules 330, 332, 334 can compriseindividual modules at separate and distinct locations (e.g., CRM, etc.).

A coarsening module 330 can include CRI that when executed by theprocessing resource 324 can provide a number of coarsening functions.The coarsening module 330 can reduce the number of nodes in thesemantics graph. The coarsening module can create a number of sub-graphscontaining supernodes within the semantics graph by matching andcollapsing matched nodes within the semantics graph.

A partitioning module 332 can include CRI that when executed by theprocessing resource 324 can perform a number of partitioning functions.The partitioning module 332 can divide (e.g. partition) each of thenumber of sub-graphs created by the coarsening module 330 into a numberof clusters.

A refining module 334 can include CRI that when executed by theprocessing resource 334 can perform a number of refining functions. Therefining module 334 can iteratively refine the number of clusterscreated by the partitioning module 332 to reduce the edge-cut of thesemantics graph, based on the number of clusters.

A memory resource 328, as used herein, can include volatile and/ornon-volatile memory. Volatile memory can include memory that dependsupon power to store information, such as various types of dynamic randomaccess memory (DRAM), among others. Non-volatile memory can includememory that does not depend upon power to store information.

The memory resource 328 can be integral, or communicatively coupled, toa computing device, in a wired and/or a wireless manner. For example,the memory resource 328 can be an internal memory, a portable memory, aportable disk, or a memory associated with another computing resource(e.g., enabling CRIs to be transferred and/or executed across a networksuch as the Internet).

The memory resource 328 can be in communication with the processingresource 324 via a communication path 326. The communication path 326can be local or remote to a machine (e.g., a computing device)associated with the processing resource 324. Examples of a localcommunication path 326 can include an electronic bus internal to amachine (e.g., a computing device) where the memory resource 328 is oneof volatile, non-volatile, fixed, and/or removable storage medium incommunication with the processing resource 324 via the electronic bus.

The communication path 326 can be such that the memory resource 328 isremote from the processing resource (e.g., 324), such as in a networkconnection between the memory resource 328 and the processing resource(e.g., 324). That is, the communication path 326 can be a networkconnection. Examples of such a network connection can include a localarea network (LAN), wide area network (WAN), personal area network(PAN), and the Internet, among others. In such examples, the memoryresource 328 can be associated with a first computing device and theprocessing resource 324 can be associated with a second computing device(e.g., a Java® server). For example, a processing resource 324 can be incommunication with a memory resource 328, wherein the memory resource328 includes a set of instructions and wherein the processing resource324 is designed to carry out the set of instructions.

The processing resource 324 coupled to the memory resource 328 canexecute CRI to cluster signifiers in a semantics graph. The processingresource 324 coupled to the memory resource 328 can also execute CRI tocreate a coarsened graph by iteratively matching a number of nodeswithin a subset of nodes in a semantics graph, and collapsing each setof matched nodes to create a supernode; create a partitioned graph fromthe coarsened graph by partitioning a number of sub-graphs in thecoarsened graph based on a vertex weight, wherein the partitioned graphincludes a reduced edge-cut as compared to the semantics graph; andreduce the edge-cut of the partitioned graph using local refinementheuristics.

As used herein, “logic” is an alternative or additional processingresource to execute the actions and/or functions, etc., describedherein, which includes hardware (e.g., various forms of transistorlogic, application specific integrated circuits (ASICs), etc.), asopposed to computer executable instructions (e.g., software, firmware,etc.) stored in memory and executable by a processor.

The specification examples provide a description of the applications anduse of the system and method of the present disclosure. Since manyexamples can be made without departing from the spirit and scope of thesystem and method of the present disclosure, this specification setsforth some of the many possible example configurations andimplementations.

What is claimed:
 1. A method comprising: generating a semantics graphthat represents content extracted from an enterprise network in the formof signifiers, wherein nodes of the semantics graph represent thesignifiers; coarsening the semantics graph of the signifiers into anumber of sub-graphs, the number of sub-graphs including a particularsub-graph that comprises a first node and a second node of the semanticsgraph; after coarsening the semantics graph into the number ofsub-graphs, splitting the particular sub-graph into multiple clusterscomprising a first cluster that includes the first node and a secondcluster that includes the second node; and after splitting theparticular sub-graph into the multiple clusters, reducing an edge-cut ofthe multiple clusters by switching the first node from the first clusterto the second cluster and switching the second node from the secondcluster to the first cluster.
 2. The method of claim 1, whereincoarsening the semantics graph comprises condensing multiple individualnodes of the semantics graph into a supernode, the supernode includingthe multiple individual nodes and incident edges of the multipleindividual nodes.
 3. The method of claim 2, wherein prior to splittingthe particular sub-graph, the particular sub-graph includes thesupernode.
 4. The method of claim 3, wherein splitting the particularsub-graph into the multiple clusters comprises: splitting the supernodeso that some of the multiple individual nodes of the supernode areincluded in the first cluster and others of the multiple individualnodes of the supernode are included in the second cluster.
 5. The methodof claim 1, wherein the semantics graph comprises a weighted semanticsgraph; and wherein splitting the particular sub-graph comprisessplitting the particular sub-graph into the multiple clusters based onweights of the nodes included in the particular sub-graph.
 6. The methodof claim 5, wherein splitting the particular sub-graph into multipleclusters based on the weights of the nodes comprises splitting theparticular sub-graph so that the first cluster and the second clusterhave equal node weights.
 7. The method of claim 1, wherein reducingcomprises iteratively reducing the edge-cut using local refinementheuristics.
 8. A non-transitory computer-readable medium storinginstructions executable by a processing resource to: generate a weightedsemantics graph that represents content extracted from an enterprisenetwork in the form of signifiers, wherein nodes of the weightedsemantics graph represent the signifiers; coarsen the weighted semanticsgraph into a number of sub-graphs, including a particular sub-graphcomprising a supernode comprising multiple individual nodes of thesemantics graph; after coarsening of the semantics graph, split theparticular sub-graph into a first cluster and a second cluster of equalweight; and after splitting the particular sub-graph into the first andsecond clusters, reduce an edge-cut of the first and second clusters byswitching an individual node in the first cluster with anotherindividual node in the second cluster.
 9. The non-transitorycomputer-readable medium of claim 8, wherein the instructions areexecutable by the processing resource to split the particular sub-graphinto the first and second clusters by assigning the supernode to thefirst cluster.
 10. The non-transitory computer-readable medium of claim8, wherein the instructions are executable by the processing resource tosplit the particular sub-graph into the first and second clusters byassigning some of the multiple individual nodes of the supernode to thefirst cluster and assigning others of the multiple individual nodes ofthe supernode to the second cluster.
 11. The non-transitorycomputer-readable medium of claim 8, wherein the instructions areexecutable by the processing resource to coarsen the weighted semanticsgraph into the number of sub-graphs in multiple iterations.
 12. Thenon-transitory computer-readable medium of claim 11, wherein theinstructions are executable by the processing resource to coarsen theweighted semantics graphs by: in a first iteration of the multipleiterations: condense a first individual node and a second individualnodes of the semantics graph into a first supernode; and in a seconditeration of the multiple iterations: condense the first supernode and athird individual node of the semantics graph to form the supernodecomprising the multiple individual nodes of the weighted semanticsgraph.
 13. The non-transitory computer-readable medium of claim 11,wherein a number individual nodes in the semantics graph is reduced witheach of the multiple iterations as individual nodes are condensed intosupernodes.
 14. A system comprising: a processing resource; and a memoryresource coupled to the processing resource, the memory resourcecomprising instructions executable by the processing resource to:generate a semantics graph that represents content extracted from anenterprise network in the form of signifiers, wherein nodes of thesemantics graph represent the signifiers; create a coarsened graph froma semantics graph, the coarsened graph comprising multiple sub-graphs,the multiple sub-graphs comprising a particular sub-graph including afirst node and a second node of the semantics graph; create apartitioned graph from the coarsened graph by splitting each of themultiple sub-graphs into multiple clusters, including splitting theparticular sub-graph itself into multiple clusters comprising a firstcluster including the first node and a second cluster including thesecond node; and reduce an edge-cut of the partitioned graph using localrefinement heuristics.
 15. The system of claim 14, wherein theinstructions are executable by the processing resource to reduce theedge-cut of the partitioned graph using local refinement heuristics bydetermining the edge-cut of the partitioned graph after switching thefirst node in the first cluster with the second node in the secondcluster.
 16. The system of claim 15, wherein the instructions executableby the processing resource to reduce the edge-cut of the partitionedgraph using local refinement heuristics further by maintaining theswitched first node and second node when the switch reduces the edge-cutof the partitioned graph.
 17. The system of claim 15, wherein theinstructions executable by the processing resource to reduce theedge-cut of the partitioned graph using local refinement heuristicsfurther by reversing the switched of the first node and the second nodewhen the switch does not reduce the edge-cut of the partitioned graph.18. The system of claim 14, wherein the semantics graph comprises aweighted semantics graph; and wherein the instructions are executable bythe processing resource to create the partitioned graph from thecoarsened graph by splitting the particular sub-graph itself intomultiple clusters of equal weight.
 19. The system of claim 14, whereinthe instructions are executable by the processing resource to create thecoarsened graph to include a supernode comprising multiple individualnodes of the semantics graph.
 20. The system of claim 19, wherein theinstructions are executable by the processing resource further togenerate the supernode by condensing the multiple individual nodes intothe supernode over multiple coarsening iterations.